Explore machine learning's essentials for software engineers, delve into supervised learning, neural networks, and deep learning, and gain skills to tackle real-world data challenges effectively.

pml_final.tar.gz

just_run

chapter06

SPA_07

SPA_gdb_frontEnd

J12-1

J12-2

SPA-server_image

Handson-10

Machine learning is the future for the next generation of software professionals.

This course serves as a guide to machine learning for software engineers. You’ll be introduced to three of the most relevant components of the AI/ML discipline; supervised learning, neural networks, and deep learning. You’ll grasp the differences between traditional programming and machine learning by hands-on development in supervised learning before building out complex distributed applications with neural networks. You’ll go even further by layering networks to create deep learning systems. You’ll work with complex real-world datasets to explore machine behavior from scratch at each phase.

By the end of this course, you’ll have a working knowledge of modern machine learning techniques. Using software engineering, you’ll be prepared to build complex neural networks and wrangle real-world data challenges.

Fundamentals of Machine Learning for Software Engineers

![](/api/collection/10370001/5351678229872640/page/5192970185211904/image/6689116602761216?page_type=collection_lesson)

![](/api/collection/10370001/5351678229872640/page/5192970185211904/image/5153400781537280?page_type=collection_lesson)

![](/api/collection/10370001/5351678229872640/page/5192970185211904/image/6197409671020544?page_type=collection_lesson)

(Select all that apply.) The linear regression might fail to deliver good results in which graphs.


(Select all that apply.) The linear regression might fail to deliver good results in which graphs.

![](/api/collection/10370001/5351678229872640/page/5192970185211904/image/4628579266068480?page_type=collection_lesson)

![](/api/collection/10370001/5351678229872640/page/5192970185211904/image/4645957559386112?page_type=collection_lesson)


![](/api/collection/10370001/5351678229872640/page/5192970185211904/image/5203319978459136?page_type=collection_lesson)

Which plot represents the **sigmoid function**?

Which plot represents the sigmoid function?

Calculate false positives (**FP**) and false negatives (**FN**) for a binary classifier that detects the digit $5$.

![](/api/collection/10370001/5351678229872640/page/5192970185211904/image/5649802364190720?page_type=collection_lesson)


Calculate false positives (FP) and false negatives (FN) for a binary classifier that detects the digit <math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>5</mn></mrow><annotation encoding="application/x-tex">5</annotation></semantics></math>5.
<img src="/api/collection/10370001/5351678229872640/page/5192970185211904/image/5649802364190720?page_type=collection_lesson" alt="" />

(Select all that apply.) Which of the attributes should a suitable wrapper have?

(Select all that apply.) Which of the attributes should a suitable wrapper have?

How does flattening the input image for the training phase affect accuracy?

How does flattening the input image for the training phase affect accuracy?

How Machine Learning Works

Our First Learning Program

Walking the Gradient

Hyperspace

A Discern Machine

Get Real

The Final Challenge

The Perceptron

Designing the Network

Building the Network

Training the Network

How Classifiers Work

Batchin’ Up

The Zen of Testing

Let’s Do Development

A Deeper Kind of Network

Defeating Overfitting

Taming Deep Networks

Beyond Vanilla Networks

Into the Deep

Quiz: A Discerning Machine and Getting Real